Blog

Category: Databases

Product Updates   Databases  

Relational Fields: Query Even More Relationships in Your Traces

Earlier this year, we introduced relational fields. Relational fields enable you to query spans based on their relationship to one other within a trace, rather...

Dogfooding   Databases  

Virtualizing Our Storage Engine

Our storage engine, affectionately known as Retriever, has served us faithfully since the earliest days of Honeycomb. It’s a tool that writes data to disk...

Tutorials   Tracing   Databases  

How to Use Relational Fields: Some Nifty Use Cases

We recently introduced relational fields, a new feature that allows you to query spans based on their relationship to each other within a trace. This...

Product Updates   Databases  

Introducing Relational Fields

Expanded fields allow you to more easily find interesting traces and learn about the spans within them, saving time for debugging and enabling more curiosity...

Databases   Ask Miss O11y  

Ask Miss O11y: Observability vs BI Tools & Data Warehouses

You probably have already answered this before, but do you have a good rule of thumb for where o11y [observability] ends and BI [business intelligence]/data...

Instrumentation   Databases   Ask Miss O11y  

Ask Miss O11y: How Can I Add o11y to Databases?

How do we bring observability to the DB world? In the SQL Server world, you can marry up perfmon and extended event traces but is...

Technical Deep Dives   Observability   Databases  

How Time Series Databases Work—and Where They Don't

In my previous post, we explored why Honeycomb is implemented as a distributed column store. Just as interesting to consider, though, is why Honeycomb is...

Product Updates   Databases  

Understanding Lambda Sleep Cycles With CONCURRENCY

CONCURRENCY is now enabled for all customers. See our docs page for information about how it works and how to use it effectively. Questions or...

Observability   Databases  

Why Observability Requires a Distributed Column Store

Honeycomb is known for its incredibly fast performance: you can sift through billions of rows, comparing high-cardinality data across thousands of fields, and get fast...

Service Level Objectives   Dogfooding   Databases  

Data Availability Isn’t Observability

But it’s better than nothing... Most of the industry is racing to adopt better observability practices, and they’re discovering lots of power in being able...

Product Updates   Databases  

Event Latency: What It Is and Why You Should Care

Recently, we added a new derived column function to Honeycomb, INGEST_TIMESTAMP(), which can help customers debug event latency and/or inaccurate timestamps. A meaningful minority of...

Software Engineering   Product Updates   Dogfooding   Databases  

From "Secondary Storage" To Just "Storage": A Tale of Lambdas, LZ4, and Garbage Collection

When we introduced Secondary Storage two years ago, it was a deliberate compromise between economy and performance. Compared to Honeycomb’s primary NVMe storage attached to...

Dogfooding   Debugging   Databases  

Stop Your Database From Hating You With This One Weird Trick

Let's not bury the lede here: we use Observability-Driven Development at Honeycomb to identify and prevent DB load issues. Like every online service, we experience...

Product Updates   Databases  

Speeding Things Up So Your Queries Can Bee Faster

Honeycomb strives to be a fast, efficient tool; our storage back-end satisfies the median customer query in 250ms (and the P90 in 1.3 seconds). Still,...

Operations   Observability   Dogfooding   Databases  

Postmortem: RDS Clogs & Cache-Refresh Crash Loops

On Thursday, October 4, we experienced a partial API outage from 21:02-21:56 UTC (14:02-14:56 PDT). Despite some remediation work, we saw a similar (though less...

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